U.S. patent application number 13/355404 was filed with the patent office on 2012-08-16 for respiratory event alert system.
This patent application is currently assigned to Masimo Corporation. Invention is credited to Greg A. Olsen, Phil Weber.
Application Number | 20120209084 13/355404 |
Document ID | / |
Family ID | 46637405 |
Filed Date | 2012-08-16 |
United States Patent
Application |
20120209084 |
Kind Code |
A1 |
Olsen; Greg A. ; et
al. |
August 16, 2012 |
RESPIRATORY EVENT ALERT SYSTEM
Abstract
A respiratory monitoring system can include an acoustic
respiratory sensor that obtains acoustic information from a patient
and one or more processors in communication with the acoustic
respiratory sensor. Such processors can receive a respiratory rate
value derived from the acoustic information. This respiratory rate
value can reflect an averaged set of respiratory rate values over a
period of time. The one or more processors can also receive an
indication of a respiratory abnormality that occurred during the
same time period. Further, the one or more processors can output
the averaged respiratory rate value together with a respiratory
event indicator reflecting the respiratory abnormality.
Inventors: |
Olsen; Greg A.; (Trabuco
Canyon, CA) ; Weber; Phil; (Solano Beach,
CA) |
Assignee: |
Masimo Corporation
Irvine
CA
|
Family ID: |
46637405 |
Appl. No.: |
13/355404 |
Filed: |
January 20, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61435130 |
Jan 21, 2011 |
|
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Current U.S.
Class: |
600/301 ;
600/529 |
Current CPC
Class: |
A61B 5/68335 20170801;
A61B 5/7282 20130101; A61B 5/4818 20130101; A61B 7/003 20130101;
A61B 5/743 20130101; A61B 7/04 20130101; A61B 2562/0204 20130101;
A61B 5/0205 20130101; A61B 5/0816 20130101; A61B 5/7221
20130101 |
Class at
Publication: |
600/301 ;
600/529 |
International
Class: |
A61B 5/0205 20060101
A61B005/0205; A61B 5/00 20060101 A61B005/00; A61B 7/00 20060101
A61B007/00; A61B 5/08 20060101 A61B005/08 |
Claims
1. A respiratory monitoring system for indicating respiratory
abnormalities on a physiological monitor display, the system
comprising: an acoustic respiratory sensor configured to obtain
acoustic information from a patient, the acoustic information
reflecting one or more physiological parameters of the patient; and
one or more processors in communication with the acoustic
respiratory sensor, the one or more processors configured to:
receive a respiratory rate value, the respiratory rate value
reflecting a respiratory measurement obtained from the acoustic
information averaged over a time period, receive an indication of a
respiratory abnormality occurring during the time period, and
output the respiratory rate value together with a discrete
respiratory event indicator reflecting the respiratory
abnormality.
2. The respiratory monitoring system of claim 1, wherein the
respiratory rate value does not directly indicate an occurrence of
the respiratory abnormality.
3. The respiratory monitoring system of claim 1, wherein the
respiratory abnormality reflects an apnea or hypopnea event.
4. The respiratory monitoring system of claim 1, wherein the
respiratory event indicator further reflects a severity of the
respiratory abnormality.
5. The respiratory monitoring system of claim 4, wherein a shape of
the respiratory event indicator is configured to reflect a severity
of the respiratory abnormality.
6. The respiratory monitoring system of claim 4, wherein the
respiratory event indicator is further configured to indicate a
calculated confidence in detecting the respiratory abnormality.
7. The respiratory monitoring system of claim 1, wherein the one or
more processors are further configured to receive physiological
information from one or more additional physiological sensors
coupled with the patient, the physiological information reflecting
the respiratory abnormality.
8. The respiratory monitoring system of claim 7, wherein the one or
more processors are further configured to adjust a configuration of
the respiratory event indicator responsive to the physiological
information.
9. The respiratory monitoring system of claim 8, wherein the
physiological information comprises one or more values for one or
more of the following physiological parameters: oxygen saturation,
hemoglobin, and plethysmograph variability.
10. The respiratory monitoring system of claim 1, wherein the one
or more processors are further configured to cause display of
additional detail regarding the indication of the respiratory
abnormality in response to user input.
11. A method for indicating respiratory abnormalities on a
physiological monitor display, the method comprising: by one or
more processors: receiving a respiratory rate value from a
respiratory rate calculator, the respiratory rate value
corresponding to respiration of a patient averaged over a time
period, such that the respiratory rate value does not directly
indicate abnormal respiration in the patient; outputting the
respiratory value on a physiological monitor display for
presentation to a clinician; receiving an indication of a
respiratory abnormality occurring during the time period; and
outputting an indicator reflecting the respiratory abnormality on
the physiological monitor display.
12. The method of claim 11, wherein the respiratory abnormality
reflects a respiratory pause.
13. The method of claim 11, wherein the respiratory abnormality
reflects one or more of the following conditions: apnea, hypopnea,
dyspnea, bradypnea, obstruction, wheezing, striders, and
ronchi.
14. The method of claim 11 further comprising indicating a
confidence in an occurrence of the respiratory abnormality.
15. The method of claim 14, wherein said indicating the confidence
comprises indicating a relatively higher confidence in response to
receiving additional physiological information that further
reflects the respiratory abnormality.
16. The method of claim 15, wherein the additional physiological
information comprises oxygen saturation information.
17. The method of claim 15, wherein the additional physiological
information comprises hemoglobin information.
18. The method of claim 11, further comprising generating the
indicator to further reflect a length of the respiratory
abnormality.
19. The method of claim 11, further comprising generating the
indicator to further reflect a severity of the respiratory
abnormality.
20. The method of claim 11, wherein the indicator reflects one or
more of the following: a length of the respiratory abnormality, a
severity of the respiratory abnormality, and a confidence in
occurrence of the respiratory abnormality.
21. The method of claim 11, wherein said outputting the indicator
comprises superimposing the indicator over a trend graph reflecting
the respiratory rate value.
Description
CROSS REFERENCE TO RELATED APPLICATION
[0001] This application is a non-provisional of U.S. Provisional
Patent Application No. 61/435,130, filed Jan. 21, 2011, which is
hereby incorporated by reference herein in its entirety.
BACKGROUND
[0002] Hospitals, nursing homes, and other patient care facilities
typically include patient monitoring devices at one or more
bedsides in the facility. Patient monitoring devices generally
include sensors, processing equipment, and displays for obtaining
and analyzing a patient's physiological parameters. Physiological
parameters include, for example, blood pressure, respiratory rate,
oxygen saturation (SpO.sub.2) level, other blood constitutions and
combinations of constitutions, and pulse, among others. Clinicians,
including doctors, nurses, and certain other caregiver personnel
use the physiological parameters obtained from the patient to
diagnose illnesses and to prescribe treatments. Clinicians can also
use the physiological parameters to monitor a patient during
various clinical situations to determine whether to increase the
level of care given to the patient. Various patient monitoring
devices are commercially available from Masimo Corporation
("Masimo") of Irvine, Calif.
[0003] During and after surgery and in other care situations,
respiratory rate is a frequently monitored physiological parameter
of a patient. Respiratory rate can be indicated as the number of
breaths a person takes within a certain amount of time, such as
breaths per minute. For example, a clinician (such as a nurse,
doctor, or the like) can use respiratory rate measurements to
determine whether a patient is experiencing respiratory distress
and/or dysfunction.
SUMMARY OF DISCLOSURE
[0004] In certain embodiments, a respiratory monitoring system for
indicating respiratory abnormalities on a physiological monitor
display includes an acoustic respiratory sensor configured to
obtain acoustic information from a patient. The acoustic
information can reflect one or more physiological parameters of the
patient. The system can further include one or more processors in
communication with the acoustic respiratory sensor. The one or more
processors can be configured to: receive a respiratory rate value,
the respiratory rate value reflecting a respiratory measurement
obtained from the acoustic information averaged over a time period;
receive an indication of a respiratory abnormality occurring during
the time period; and output the respiratory rate value together
with a respiratory event indicator reflecting the respiratory
abnormality.
[0005] In various embodiments, a method for indicating respiratory
abnormalities on a physiological monitor display can be performed
by one or more processors. The one or more processors can receive a
respiratory rate value from a respiratory rate calculator, the
respiratory rate value corresponding to respiration of a patient
averaged over a time period, such that the respiratory rate value
does not directly indicate abnormal respiration in the patient. The
one or more processors can further output the respiratory value on
a physiological monitor display for presentation to a clinician.
Moreover, the one or more processors can receive an indication of a
respiratory abnormality occurring during the time period.
Additionally, the one or more processors can output an indicator
reflecting the respiratory abnormality on the physiological monitor
display.
[0006] For purposes of summarizing the disclosure, certain aspects,
advantages and novel features of the inventions have been described
herein. It is to be understood that not necessarily all such
advantages can be achieved in accordance with any particular
embodiment of the inventions disclosed herein. Thus, the inventions
disclosed herein can be embodied or carried out in a manner that
achieves or optimizes one advantage or group of advantages as
taught herein without necessarily achieving other advantages as can
be taught or suggested herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] Throughout the drawings, reference numbers can be re-used to
indicate correspondence between referenced elements. The drawings
are provided to illustrate embodiments of the inventions described
herein and not to limit the scope thereof.
[0008] FIG. 1 is a block diagram illustrating an embodiment of a
patient monitoring system;
[0009] FIG. 2 is a top perspective view illustrating portions of an
example sensor assembly that can be used with the patient
monitoring system of FIG. 1;
[0010] FIG. 3 is a block diagram illustrating an embodiment of a
respiratory monitoring system;
[0011] FIG. 4 illustrates an example display of respiratory trend
data and respiratory event indicators provided by the respiratory
monitoring system of FIG. 3;
[0012] FIG. 5 illustrates an embodiment of a process for displaying
respiratory data;
[0013] FIG. 6A illustrates another embodiment of a respiratory
monitoring system;
[0014] FIG. 6B illustrates an embodiment of a process for
displaying respiratory data provided by the respiratory monitoring
system of FIG. 6A;
[0015] FIGS. 7 and 8 illustrate example displays of a respiratory
monitoring system of FIG. 6A;
[0016] FIG. 9 illustrates a rating system that indicates both a
calculated confidence level in a detection of a respiratory event
and an estimated severity of the respiratory event.
DETAILED DESCRIPTION
[0017] Acoustic sensors, including piezoelectric acoustic sensors,
can be used to measure breath sounds and other biological sounds of
a patient. Breath sounds obtained from an acoustic sensor can be
processed by a patient monitor to derive one or more physiological
parameters of a patient, including respiratory rate. Respiratory
rate can also be measured using other instruments, including a
capnograph, which can measure respiratory rate from an end-tidal
carbon dioxide (EtCO.sub.2) waveform, and a pulse oximeter, which
can derive respiratory rate from a photoplethysmograph. However,
respiratory rate measurements can be an incomplete representation
of a patient's breathing events.
[0018] Currently-available respiratory monitors provide respiratory
indicators, such as a respiratory rate. Because data used to
compute respiratory rate can be noisy and/or vary over time,
respiratory rate is typically computed as an average of breaths
taken over a certain time frame. However, providing averaged
respiratory rate data alone may not capture respiratory events of
clinical interest. For example, a patient with a respiratory rate
(RR) of 20 breaths per minute averaged over 30 seconds can
subsequently have a 30 second apnea. In such a case, the monitor
would show a RR of 10 breaths per minute. In this example, the
monitor would not directly output an indication regarding the 30
second lack of breathing.
[0019] Advantageously, the respiratory monitoring systems described
herein can detect respiratory events and provide indicators of the
respiratory events along with respiratory rate data. Such
indicators of respiratory events can also convey specific
information about the respiratory event. For example, an indicator
of a respiratory event can convey an estimated severity of a
respiratory event, length of occurrence of such an event, and/or a
calculated confidence level in a detection of the respiratory
event. Respiratory event indicators can provide a clinician with an
indication that the respiratory event has been tracked and should
be observed.
[0020] This disclosure describes, among other features, systems and
methods for using one or more physiological parameter inputs to
calculate respiratory rate and detect respiratory events.
Respiratory events can include such respiratory abnormalities such
as increased respiration events, reduced respiration events, and
respiratory pause events (e.g., non-respiration events). Some
example respiratory events include, but are not limited to, apnea,
obstruction, wheezes, striders, ronchi, rales, hypopnea, dyspnea,
bradypnea, and decreased volume or change in airflow. In certain
embodiments, a patient monitoring system can output an indicator of
a respiratory event that can convey one or more characteristics of
the respiratory event, along with respiratory rate data, for
presentation to a clinician.
[0021] For purposes of illustration, this disclosure is described
primarily in the context of respiratory rate. However, the features
described herein can be applied in the context of other respiratory
parameters, including, for example, inspiratory time, expiratory
time, inspiratory to expiratory ratio, inspiratory flow, expiratory
flow, tidal volume, minute volume, changes in breath sounds, and
the like.
[0022] Referring to the drawings, FIGS. 1 and 2 illustrate example
patient monitoring systems, sensors, and cables that can be used to
derive a respiratory rate measurement from a patient. FIGS. 3
through 9 illustrate respiratory monitoring systems, along with
associated displays and processes. The features of FIGS. 3 through
9 can be implemented at least in part using the systems and sensors
described in FIGS. 1 and 2.
[0023] With reference to FIG. 1, an embodiment of a physiological
monitoring system 100 is shown. In the physiological monitoring
system 100, a patient 101 can be monitored using one or more
acoustic sensor assemblies 103, each of which can transmit one or
more signals over a cable 105 or other communication link or medium
to a physiological monitor 107. The physiological monitor 107 can
also be referred to as a patient monitor. The physiological monitor
107 can include a processor 109 and, optionally, a display 111. The
one or more acoustic sensors 103 include sensing elements such as,
for example, acoustic piezoelectric devices or the like. The
acoustic sensors 103 can generate respective signals by measuring a
physiological parameter of the patient 101. The signals can then be
processed by one or more processors 109. The one or more processors
109 can then communicate the processed signal to the display 111.
In an embodiment, the display 111 is incorporated in the
physiological monitor 107. In another embodiment, the display 111
is separate from the physiological monitor 107. In one embodiment,
the monitoring system 100 is a portable monitoring system. In
another embodiment, the monitoring system 100 is a pod, without a
display, that is adapted to provide physiological parameter data to
a display.
[0024] For clarity, a single block is used to illustrate the one or
more acoustic sensors 103 shown in FIG. 1. It should be understood
that the acoustic sensor 103 shown is intended to represent one or
more sensors. In an embodiment, the one or more acoustic sensors
103 include a single sensor of one of the types described below. In
another embodiment, the one or more acoustic sensors 103 include at
least two acoustic sensors. Other combinations of numbers and types
of sensors are also suitable for use with the physiological
monitoring system 100.
[0025] In some embodiments of the system shown in FIG. 1, all of
the hardware used to receive and process signals from the sensors
are housed within the same housing. In other embodiments, some of
the hardware used to receive and process signals is housed within a
separate housing. In addition, the physiological monitor 107 of
certain embodiments includes hardware, software, or both hardware
and software, whether in one housing or multiple housings, used to
receive and process the signals transmitted by the sensors 103.
[0026] FIG. 2 illustrates an embodiment of a sensor system 200
including a sensor assembly 201 and a monitor cable 211 suitable
for use with the physiological monitor shown in FIG. 1. For
example, the sensor system 200 can implement the acoustic sensor
103 (FIG. 1) that can be used to acquire respiratory data. The
sensor assembly 201 can include a sensor 215, a cable assembly 217,
and a connector 205. The sensor 215, in one embodiment, includes a
sensor subassembly 202 and an attachment subassembly 204. The cable
assembly 217 of one embodiment includes a sensor cable 207 and a
patient anchor 203. A sensor connector subassembly 205 can be
connected to the sensor cable 207.
[0027] The sensor connector subassembly 205 can be removably
attached to an instrument cable 211 via an instrument cable
connector 209. The instrument cable 211 can be attached to a cable
hub 220, which can include a port 221 for receiving a connector 212
of the instrument cable 211 and a second port 223 for receiving
another cable. In certain embodiments, the second port 223 can
receive a cable connected to a pulse oximetry or other sensor. In
addition, the cable hub 220 can include additional ports in other
embodiments for receiving additional cables. The hub can include a
cable 222 which terminates in a connector 224 adapted to connect to
a physiological monitor (not shown).
[0028] The sensor connector subassembly 205 and connector 209 can
allow the sensor connector 205 to be straightforwardly and
efficiently joined with and detached from the connector 209.
Embodiments of connectors having connection mechanisms that can be
used for the connectors 205, 209 are described in U.S. patent
application Ser. No. 12/248,856 (hereinafter referred to as "the
'856 application"), filed on Oct. 9, 2008, which is incorporated in
its entirety by reference herein. For example, the sensor connector
205 could include a mating feature (not shown) which mates with a
corresponding feature (not shown) on the connector 209. The mating
feature can include a protrusion which engages in a snap fit with a
recess on the connector 209. In certain embodiments, the sensor
connector 205 can be detached via one hand operation, for example.
Examples of connection mechanisms can be found specifically in
paragraphs [0042], [0050], [0051], [0061]-[0068] and [0079], and
with respect to FIGS. 8A-F, 13A-E, 19A-F, 23A-D and 24A-C of the
'856 application, for example.
[0029] The sensor connector subassembly 205 and connector 209 can
reduce the amount of unshielded area in and generally provide
enhanced shielding of the electrical connection between the sensor
and monitor in certain embodiments. Examples of such shielding
mechanisms are disclosed in the '856 application in paragraphs
[0043]-[0053], [0060] and with respect to FIGS. 9A-C, 11A-E, 13A-E,
14A-B, 15A-C, and 16A-E, for example.
[0030] In an embodiment, the acoustic sensor assembly 201 includes
a sensing element, such as, for example, a piezoelectric device or
other acoustic sensing device. The sensing element can generate a
voltage that is responsive to vibrations generated by the patient,
and the sensor can include circuitry to transmit the voltage
generated by the sensing element to a processor for processing. In
an embodiment, the acoustic sensor assembly 201 can include
circuitry for detecting and transmitting information related to
biological sounds to a physiological monitor. These biological
sounds can include heart, breathing, and/or digestive system
sounds, in addition to many other physiological phenomena. The
acoustic sensor 215 in certain embodiments is a biological sound
sensor, such as the sensors described herein. In some embodiments,
the biological sound sensor is one of the sensors such as those
described in U.S. patent application Ser. No. 12/044,883, filed
Mar. 7, 2008, entitled "Systems and Methods for Determining a
Physiological Condition Using an Acoustic Monitor," (hereinafter
referred to as "the '883 application"), the disclosure of which is
hereby incorporated by reference in its entirety. In other
embodiments, the acoustic sensor 215 is a biological sound sensor
such as those described in U.S. Pat. No. 6,661,161, which is
incorporated by reference herein in its entirety. Other embodiments
include other suitable acoustic sensors.
[0031] The attachment sub-assembly 204 can include first and second
elongate portions 206, 208. The first and second elongate portions
206, 208 can include patient adhesive (e.g., in some embodiments,
tape, glue, a suction device, etc.). The adhesive on the elongate
portions 206, 208 can be used to secure the sensor subassembly 202
to a patient's skin. One or more elongate members 210 included in
the first and/or second elongate portions 206, 208 can beneficially
bias the sensor subassembly 202 in tension against the patient's
skin and reduce stress on the connection between the patient
adhesive and the skin. A removable backing can be provided with the
patient adhesive to protect the adhesive surface prior to affixing
to a patient's skin.
[0032] The sensor cable 207 can be electrically coupled to the
sensor subassembly 202 via a printed circuit board ("PCB") (not
shown) in the sensor subassembly 202. Through this contact,
electrical signals can be communicated from the multi-parameter
sensor subassembly to the physiological monitor through the sensor
cable 207 and the cable 211.
[0033] In various embodiments, not all of the components
illustrated in FIG. 2 are included in the sensor system 200. For
example, in various embodiments, one or more of the patient anchor
203 and the attachment subassembly 204 are not included. In one
embodiment, for example, a bandage or tape is used instead of the
attachment subassembly 204 to attach the sensor subassembly 202 to
the measurement site. Moreover, such bandages or tapes can be a
variety of different shapes including generally elongate, circular
and oval, for example. In addition, the cable hub 220 need not be
included in certain embodiments. For example, multiple cables from
different sensors could connect to a monitor directly without using
the cable hub 220.
[0034] Additional information relating to acoustic sensors
compatible with embodiments described herein, including other
embodiments of interfaces with the physiological monitor, are
included in the '883 application. An example of an acoustic sensor
that can be used with the embodiments described herein is disclosed
in U.S. Patent Application No. 61/252,076, filed Oct. 15, 2009,
titled "Acoustic Sensor Assembly," the disclosure of which is
hereby incorporated by reference in its entirety.
[0035] FIG. 3 is a block diagram illustrating an embodiment of a
respiratory monitoring system 300. The respiratory monitoring
system 300 can be used to process respiratory data obtained from
one or more sensors, which can include, for example, the acoustic
sensor 103 (FIG. 1), the sensor assembly 201 (FIG. 2), or any of
the sensors described herein. The respiratory monitoring system 300
can be included as part of the physiological monitor 107 (FIG. 1).
The illustrated respiratory monitoring system 300 includes a
physiological parameter calculator 310 and a display 320.
[0036] The physiological parameter calculator 310 can be included,
for example, as part of the processor 109 (FIG. 1). Respiratory
data can be provided to the physiological parameter calculator 310.
The physiological parameter calculator 310 can include a
respiratory rate calculator 312 and a respiratory event detector
314. The respiratory rate calculator 312 can determine a
respiratory rate based at least partly on the respiratory data. The
respiratory event detector 314 can detect respiratory events and
provide associated data. The physiological parameter calculator 310
can provide both respiratory rate data and respiratory event data
to the display 320. Alternatively or additionally, the respiratory
rate data and the respiratory event data can be provided to other
user devices. The respiratory rate data and the respiratory event
data can be provided though wires or over a network.
[0037] The respiratory rate calculator 312 can derive a respiratory
rate from respiratory sensor data, for example, data provided by
the sensor of FIG. 2. A breath can be detected by identifying
certain features, such as a peak or trough, on a waveform of the
respiratory data. Then a respiratory rate can be computed as the
number of breaths detected in a certain time period. For example, a
breath can be identified as a peak or trough that satisfies a
predetermined threshold value. Respiratory rate can be expressed in
breaths per minute. One example system for calculating respiratory
rate, apnea, and other respiratory characteristics, which can be
used with any of the embodiments described herein, is described in
U.S. Publication No. 2007/0282212, filed Jun. 19, 2007, titled
"Non-Invasive Monitoring of Respiratory Rate, Heart Rate, and
Apnea," attorney docket number MCAN.019NP, the disclosure of which
is hereby incorporated by reference in its entirety.
[0038] Due to noisy respiratory data and/or variability in
breathing, the respiratory rate can be averaged over time, such as
30 seconds, 60 seconds, or some other time period. Averaging can
provide more robust and accurate respiratory data. At the same
time, averaging respiratory rate over time can provide an
incomplete representation of a patient's breathing events. For
example, certain significant variations in respiratory rate within
the averaging time may be lost. Such variations can reflect any of
the respiratory abnormalities or events described herein.
[0039] The respiratory event detector 314 can detect one or more
respiratory events from the respiratory sensor data. The one or
more respiratory events can be respiratory abnormalities, increased
respiration events, reduced respiration events, and/or
non-respiration events. Example respiratory events can include, but
are not limited to, apnea, obstruction, wheezes, striders, ronchi,
rales, hypopnea, and breath sounds such as decreased volume or
change in airflow. In some embodiments, the respiratory event
detector 314 can provide data indicating a particular respiratory
event.
[0040] Respiratory events can be detected a variety of ways.
Detecting different events can include a variety of signal
processing techniques. For example, reduced respiration events,
such as apnea, can be identified by a cessation in breathing over a
certain period of time. Other respiratory events can be detected by
identifying patterns in breathing that are indicative of different
respiratory problems. For example, apnea may be detected by a
cessation of breathing for more than a predetermined period of
time.
[0041] The respiratory event detector 314 can also determine
characteristics of the detected respiratory event. Such
characteristics can include an estimated severity of a respiratory
event and/or a calculated confidence level in a detection of the
respiratory event. The estimated severity of an event can be based
on any indicator that a patient should require more or less medical
attention. The estimated severity can be represented by, for
example, a point indicator. A higher point indicator can be
reflective of a higher severity and a lower point indicator can be
reflective of a lower severity. More detail about how the estimated
severity can be displayed will be provided below in connection with
FIGS. 4 and 9.
[0042] The estimated severity of an event can be based on, for
example, one or more of a duration of an event, a number of
occurrences of the event within a predetermined time period, an
intensity of an event as determined by features of the acoustic
respiratory data, combinations of the same, or the like.
Alternatively or additionally, the estimated severity of a
respiratory event can be based at least partly on the type of
respiratory event detected. For example, an apnea event can have a
higher estimated severity than a hypopnea event. As another
example, a rales event can have a lower estimated severity than an
obstruction event.
[0043] The estimated severity of a respiratory event and/or
confidence level in detecting the respiratory event can be
presented as one or more respiratory event indicators to a
clinician on a display 320. The display 320 can be part of and/or
separate from the patient monitor, for example, the physiological
monitor 107 (FIG. 1). The display can implement any of the features
of the display 111 (FIG. 1).
[0044] The display 320 can present, among other things, a
respiratory rate indicator, along with the one or more respiratory
event indicators. The respiratory rate indicator can be represented
by, for example, a trend graph, a set of discrete points, a number
representing an average or moving average of a respiratory rate
over a predetermined period of time, a series of numbers, or audio
representations.
[0045] Each of the one or more respiratory event indicators can be
represented visually and/or aurally in any manner that communicates
certain characteristics of the respiratory event, for example, an
estimated severity and/or a calculated confidence level. For
example, respiratory event indicators can visually provide
information based at least party on a shape, a pattern, a color, a
flash of light, a number, and/or a word. As another example,
respiratory event indicators can aurally provide information based
at least party on a volume, a tone, a frequency, a rhythm, and/or
specific words. Moreover, respiratory event indicators can present
information both visually and aurally. In some embodiments,
respiratory event indicators can also be presented by other
methods, such as vibrations (e.g., similar to a mobile phone in
vibrate mode).
[0046] Respiratory event indicators that represent both the
estimated severity and the calculated confidence level can present
these parameters together or separately. For example, one symbol
can represent both of these parameters or one symbol can represent
each of these parameters. An example rating system incorporating
both the confidence level and the estimated severity is provided
below in connection with FIG. 9.
[0047] FIG. 4 illustrates an example display 400 of respiratory
trend data and respiratory event indicators from a respiratory
monitoring system of FIG. 3. For example, the display 400 is an
example of the display 320 (FIG. 3). The display 400 includes a
respiratory rate trend graph 410 and a current respiratory rate
indicator 420. The respiratory rate trend graph 410 can represent
trends in respiratory rate data provided by the respiratory rate
calculator 312 (FIG. 3) over a time window. The current respiratory
rate indicator 420 can provide the most recent respiratory rate
computed by the respiratory rate calculator 312 (FIG. 3).
[0048] The respiratory rate trend graph 410 can be presented along
with respiratory event indicators 412, 414, 416, 418 that indicate
the estimated severity and/or the calculated confidence level
determined by the respiratory event detector 314 (FIG. 3). Each of
the respiratory event indicators 412, 414, 416, 418 is represented
by different shapes. In this embodiment, the different shapes can
represent varying levels of estimated severity of the respiratory
event. In addition, in this embodiment, a fill of the shape can
represent the calculated confidence level of a detection of the
respiratory event.
[0049] A first respiratory event indicator 412 can represent the
lowest severity of the illustrated respiratory event indicators
412, 414, 416, 418. As the respiratory rate on the respiratory rate
trend graph 410 decreases, the severity of the indicators can
increase. This can be a result of detecting a respiratory event for
a longer duration or more than one time. For example, the
respiratory rate can decrease on the respiratory rate trend graph
410 due to a longer apnea event or more than one apnea event, and
this in turn is reflected in the estimated severity.
[0050] As the respiratory rate decreases on the respiratory rate
trend graph 410 decreases, the confidence level of detecting the
respiratory event can also increase. The decrease in respiratory
rate can correlate with certain respiratory events, such as apnea,
and thus increase the confidence in detecting such events. The
respiratory event indicators 412, 414 represented by outlines of
shapes can indicate a lower confidence level than the respiratory
event indicators 416, 418 that are represented by solid shapes.
[0051] With reference again to FIG. 3, the various respiratory
event indicators shown can reflect different estimated severities
of respiratory events. Table 1 provides an example of respiratory
data from which the respiratory event detector 314 of FIG. 3 can
determine an estimated severity of a detected apnea event. Based on
the duration of the apnea event, the estimated severity can be
represented by a point indicator. Apnea events that occur for a
longer duration of time can be represented by a higher point
indicator to reflect that the apnea event is more severe. For
example, an apnea event of less than 10 seconds can correspond to a
4 point indicator (e.g., a 4-point star or other shape) and an
apnea event of more than 30 seconds can correspond to a 12 point
indicator (e.g., a 12-point star or other shape). More than one
occurrence of an apnea event can also be reflected in a higher
point indicator. For example, a single respiratory pause event of
less than 10 seconds can correspond to a 4 point indicator and a
second apnea event of less than 10 seconds can correspond to a 5
point indicator.
TABLE-US-00001 TABLE 1 Parameter Example Event Time Output
Respiratory Apnea <10 Sec 4 Point Indicator Rate (RR) RR Apnea
<10 Sec X # 5 Point Indicator Times RR Apnea 11-19 Sec 7 Point
Indicator RR Apnea 20-30 Sec. 9 Point Indicator RR Apnea >30
sec. 12 Point Indicator
[0052] Alternatively or in addition to an estimated severity of a
respiratory event, the respiratory event detector 314 can determine
a calculated confidence level in detecting the respiratory event.
The confidence level of detecting the respiratory event can
correlate with an occurrence of the respiratory event. The
calculated confidence level can be represented a number of ways,
for example, by a color and/or a pattern.
[0053] The calculated confidence level can be based on any
additional indicator that a respiratory event has been properly
detected. The calculated confidence level of detecting a event can
be based on, for example, one or more of a duration of event, a
number of occurrences of an event, a more pronounced event as
determined by features of the respiratory data, a determination of
noise below a predetermined threshold in the respiratory data, or
other parameters calculated based on the respiratory data.
Alternatively or additionally, the calculated confidence level can
be based at least partly on the type of respiratory event detected.
For example, in certain embodiments, certain respiratory events can
be more reliably detected than others.
[0054] Table 2 provides an example of respiratory data from which a
calculated confidence level can be determined. This table provides
example respiratory event indicators for when the respiratory event
detector 314 detects an apnea event. Based on other information
related to the respiratory data provided by, for example, an
acoustic sensor, the respiratory event detector 314 can represent
the calculated confidence level of an apnea event by a color. As
shown in Table 2, an apnea event can have a white indicator when an
apnea event is detected. When the detection of the apnea event is
also supported by one or more confidence indicators, the calculated
confidence level can be represented by a different color. As shown
in Table 2, when an apnea event is detected and a respiratory rate
is below a threshold, an apnea event can be represented by a silver
indicator. The silver indicator represents a higher confidence
level because having a respiratory rate below a threshold makes it
more likely that an apnea event has been properly detected. A
second confidence indicator can further raise the confidence level
of properly detecting a respiratory event. When an apnea event is
detected, a respiratory rate is below a threshold, and the
respiratory data has been determined to have a noise level below a
threshold, an apnea event can be represented by a gold indicator.
The gold indicator can represent a higher calculated confidence
level than the silver or white indicators because a low noise level
makes it even more likely that an apnea event was properly
detected.
[0055] As also shown in Table 2, respiratory event indicators can
indicate both an estimated severity of a respiratory event and a
calculated confidence level in a detection of the respiratory
event. The estimated severity shown in the example of Table 2 can
be represented by a point indicator that can be determined based on
the duration of an apnea event, as described in connection with
Table 1. Some confidence indicators can be independent of the
estimated severity. For example, low noise respiratory data can
increase a confidence level of detecting a respiratory event
regardless of the estimated severity of the respiratory event.
Other confidence indicators can be related to the estimated
severity of the respiratory event. For example, a lower respiratory
rate threshold may be used to increase the calculated confidence
level of detecting a longer, more severe apnea event. Alternatively
or additionally, yet other confidence indicators can be partly
independent of and partly related to the estimated severity. For
example, such confidence level indicators can be independent of the
estimated severity at lower levels of estimated severity and
related to the estimated severity at higher levels of estimated
severity.
TABLE-US-00002 TABLE 2 Parameter Example Event Time Output RR Apnea
<10 Sec White 4 Point Indicator RR Apnea <15 Sec White 7
Point Indicator RR Apnea <30 Sec White 12 Point Indicator RR
Apnea <10 Sec Silver 4 Point RR <20 Indicator RR Apnea <15
Sec Silver 7 Point RR <15 Indicator RR Apnea <30 Sec Silver
12 Point RR <10 Indicator RR Apnea <10 Sec Silver 4 Point RR
<20 Indicator Low Noise Respiratory Data RR Apnea <15 Sec
Silver 7 Point RR <15 Indicator Low Noise Respiratory Data RR
Apnea <30 Sec Silver 12 Point RR <10 Indicator Low Noise
Respiratory Data
[0056] FIG. 5 illustrates an embodiment of a process 500 for
displaying respiratory data. The process 500 can be used to present
respiratory information to the display 400 (FIG. 4). The process
500 can be implemented by the respiratory monitoring system 300
(FIG. 3) described above or by any of the other systems described
herein. Each block of the process 500 can be implemented in
software, hardware, firmware, or a combination of the same.
Further, one or more blocks can be implemented using separate
modules, processors, or the like. Advantageously, the process 500
can receive respiratory data as an input and generate a respiratory
rate and an indicator of a respiratory event as outputs. As a
result, a clinician can be informed of a respiratory event and take
appropriate measures to care for the patient.
[0057] At block 502 of the process 500, respiratory data is
received. The respiratory data can be obtained from any respiratory
sensor coupled to a patient, for example, acoustic sensors 103
(FIG. 1), 201 (FIG. 2). The respiratory data can be provided from a
respiratory sensor with or without pre-processing. For example, in
some embodiments, high frequency noise can be filtered out with a
low pass filter.
[0058] At block 504 of the process a respiratory rate is obtained
from the respiratory data. The respiratory rate can be computed,
for example, by the respiratory rate calculator 312 (FIG. 3).
[0059] At block 506, respiratory events are detected, for example,
as described above in reference to the respiratory event detector
314 (FIG. 3). If at decision block 508, a respiratory event is not
detected, then the respiratory rate is output at block 510. The
respiratory rate can then be presented on any of the displays
described herein.
[0060] Alternatively, if at decision block 508 a respiratory event
is detected, then the process 500 proceeds to block 520. At block
520, an estimated severity of the respiratory event is determined.
This can be implemented by the any of the techniques described in
connection with the respiratory event detector 314 (FIG. 3). At
block 522 a confidence level in detecting the respiratory event is
determined. This can be implemented by the any of the techniques
described in connection with the respiratory event detector 314
(FIG. 3). As described above with reference to FIG. 3, the
confidence level and the estimated severity can be related and the
determination of one can be dependent on the other. Thus, in some
embodiments, blocks 520 and 522 can be implemented simultaneously,
in a different order, and/or provide results that are based on the
other block. Yet in other embodiments, only one of an estimated
significance and a confidence level are determined.
[0061] After the estimated severity and/or the confidence level are
determined, at block 524 the respiratory rate and an indicator of
the respiratory event are output. The indicator of the respiratory
event can be any indicator described herein. The respiratory rate
and the respiratory event indicator can then be presented on any of
the displays described herein, for example, as shown in FIG. 4, 7,
or 8.
[0062] Thus, in certain embodiments, the process 500 can provide
respiratory rate information along with an indicator of a
respiratory event. Advantageously, in certain embodiments, the
process 500 can therefore provide a more complete representation of
a patient's breathing events than providing respiratory rate data
alone.
[0063] The systems, displays, and processes described above can
include additional features for providing respiratory event
indicators. For example, in some embodiments, a patient monitor can
receive additional physiological data from additional sensors
and/or user input devices. The additional physiological data can be
used to obtain one or more additional parameters and/or detect
respiratory events. Advantageously, in some embodiments, the one or
more additional parameters can be used to determine the estimated
severity and/or the calculated confidence level related to a
respiratory event. FIG. 6A provides an example on one such
embodiment.
[0064] FIG. 6A illustrates a respiratory monitoring system 600A.
The respiratory monitoring system 600A can include a plurality of
sensors 602, 604, 606, 608, 610 and a user input device 612 for
obtaining physiological data. The physiological data can be
provided to a patient monitor 620 for processing. The patient
monitor 620 can then provide respiratory data to user devices 642,
644, 646, 648 over a network 630 and/or to a display 650.
[0065] The plurality of sensors 602, 604, 606, 608, 610 can be
coupled to a patient to obtain physiological data and to provide
the physiological data to the patient monitor 620. The
physiological data can be used to, among other things, determine
respiratory rate and detect respiratory events. An acoustic
respiratory sensor 602 can include any of the features of the
acoustic sensors described herein. Additional sensors 604, 606,
608, 610 can also be provided to calculate one or more additional
parameters that can be used to detect a confidence level or an
estimated severity of a respiratory event.
[0066] The additional sensors 604, 606, 608, 610 can provide one or
more additional respiratory signals that can be used to detect any
of the respiratory events described above. The one or more
additional respiratory signals can be used in place of or in
connection with the acoustic respiratory data described above to
detect respiratory events. For example, certain respiratory events
can more reliably be detected from signals provided by certain
sensors. As another example, some respiratory events can be more
reliably detected by a combination of signals from one or more
additional sensors to the acoustic sensor.
[0067] For example, an optical sensor 604 can use spectrophotometry
techniques to measure a variety of additional physiological
parameters, including, for example, oxygen saturation, hemoglobin,
methemoglobin, carboxyhemoglobin, other hemoglobin species,
glucose, concentrations of the same, plethysmograph variability,
pulse rate, perfusion, and the like. The optical sensor 604 can
also provide a plethysmograph, which can be used to detect
respiratory events. The optical sensor 604 can be a pulse oximetry
sensor, a co-oximetry sensor, a glucose sensor, or the like.
[0068] As another example, an electrical sensor, such as an
electrocardiograph (ECG) sensor 606, can be used to calculate
additional parameters and provide an ECG signal for use in
detecting a respiratory event. As yet another example, a
bioimpedance sensor 608, such as an electrode or an
ECG/defibrillator pad, can obtain bioimpedance data from a patient.
The bioimpedance data can then be used to calculate additional
parameters or in detecting a respiratory event. Moreover, other
sensor(s) 610 can also be used to obtain additional physiological
data and determine additional physiological parameters that can
assist with the detection of a respiratory event and/or determine
characteristics of the respiratory event.
[0069] Physiological data can also be obtained from the user input
device 612. The user input device can enable a user to input
patient data to the patient monitor 620. Patient data can include
without limitation patient demographics and/or medical history.
Patient data can be provided to the respiratory event calculator
624 for use determining a significance and/or confidence of a
respiratory event. Some examples of a user device 612 include a
keyboard, mouse, touch screen, or the like. The user input device
612 can be part of or separate from the patient monitor 620. In
certain embodiments, one or more of the user devices 642, 644, 646,
648 can be used to provide patient data to the patient monitor 620
via the network 630.
[0070] The physiological data obtained from the plurality of
sensors 602, 604, 606, 608, 610 and the user input device 612 can
be provided to the patient monitor 620. The patient monitor 620 can
include a respiratory rate calculator 622, a respiratory event
detector 624, and a display module 626. The respiratory rate
calculator 622 can implement any combination of the features of the
respiratory rate calculator 312 (FIG. 3).
[0071] The respiratory event detector 624 can detect a respiratory
event from physiological data obtained from one or more of the
sensors 602, 604, 606, 608, 610. The respiratory event detector 624
can implement any combination of the features of the respiratory
event detector 314 (FIG. 3) and detect any of the respiratory
events described herein. The respiratory event detector 624 can
also incorporate additional parameters obtained from one or more of
the sensors 602, 604, 606, 608, 610 into a determining a calculated
confidence level of detecting a respiratory event. Additionally,
the respiratory event detector 624 can use additional parameters
obtained from one or more of the sensors 602, 604, 606, 608, 610
and the user input device 612 in determining an estimated severity
of the respiratory event. More detail about calculating the
confidence level and the estimated severity will be provided later
with reference to FIG. 6B and Table 3.
[0072] The display module 626 can interface with the display 650.
The display module 626 can provide data to the display 650 to
provide any combination of features of the example displays of FIG.
4, 8, or 9.
[0073] The patient monitor 620 can communicate with user devices
642, 644, 646, 648 via a network 630. The network 630 can include a
wired and/or wireless environment. Some examples of communications
protocols implemented by the network 630 can include Ethernet, WiFi
(WLAN), Bluetooth, and the like. The user devices 642, 644, 646,
648 can include, but are not limited to, clinician devices, pagers,
PDAs, laptops, and the like.
[0074] The display 650 can implement the features of any of the
displays described above. The display 650 can be implemented
separate from the patient monitor 620 and/or implemented as part of
the patient monitor 620. For example, in one embodiment, the
patient monitor 620 can be implemented as an original equipment
manufacturer (OEM) board that can be incorporated with another
patient monitor that includes a display. The display 650 can also
display additional parameters and/or patient data. Examples of the
display 650 can include the displays shown in FIGS. 7 and 8, which
are described in more detail below. In addition, the display can
provide indicators of a respiratory event according to the rating
system described below in connection with FIG. 9.
[0075] FIG. 6B illustrates an embodiment of a process 600B for
displaying respiratory data from the respiratory monitoring system
of FIG. 6A. In the process 600B, physiological data can be received
from multiple sources and used to obtain a respiratory rate and an
indicator of a respiratory event. Advantageously, any of the
physiological data obtained can be used in determining a confidence
level of detecting a respiratory event or determining an estimated
severity of a respiratory event.
[0076] At block 660, respiratory data can be received from an
acoustic sensor, for example, the acoustic sensor 602 of FIG. 6A.
Then at block 662, a respiratory rate can be obtained from the data
provided by the acoustic sensor.
[0077] At block 664, oxygen saturation data can be received from an
optical sensor, for example, the optical sensor 604 of FIG. 6A.
Then at block 666, an indicator of oxygen saturation, such as SpO2,
can be obtained. For example, SpO2 can represent oxygenation, which
can be an indirect indicator of breathing. An increase in
respiratory rate can correlate with an increase in SpO2.
Conversely, a decrease in respiratory rate can correlate with a
decrease in SpO2. Therefore, advantageously, changes in SpO2 can be
useful in determining a confidence level of detecting a respiratory
event. Moreover, deviations from baseline SpO2 values can also
indicate an estimated severity of a respiratory event.
[0078] At block 668 other physiological data can be received from
one or more of the sensors 602, 604, 606, 608, 610 or a user input
device 612 of FIG. 6. The other physiological data can be used to
obtain other physiological indicators at block 670. Some example
parameters can include, but are not limited to, total hemoglobin
(e.g., SpHb or Hbt), other forms of hemoglobin (e.g., methemoglobin
or carboxyhemoglobin), plethsymograph variability (e.g., as
measured by a plethysmograph variability index or PVI), a patient
wellness index, patient demographic data, and patient history.
Measures of hemoglobin, such as SpHb, can be reflective of
bleeding, transfusion, and/or hemo-dilution/hemo-concentration. A
decrease in SpHb can indicate that a respiratory system is
distressed. Similarly, an increase in a PVI value can also indicate
that the respiratory system is distressed. For example, when an
asthma event occurs, PVI may increase. A patient wellness index can
indicate a general heath of a patient, and thus respiratory event
can be more severe for a patient with a lower patient wellness
index. Alternatively or additionally, a patient with a low patient
wellness index can be more likely to encounter a respiratory event,
and therefore there can be a higher confidence in detecting a
respiratory event when a patient has a decreased patient wellness
index. Patient demographic data (e.g., age, weight, sex, etc.) can
also factor into the confidence level in detecting a respiratory
event or an estimated severity of the respiratory event. Further, a
patient's known history can also be useful in detecting a
respiratory event, especially history of previous occurrences of
certain respiratory events.
[0079] At decision block 680, it is determined whether a
respiratory event has been detected. When a respiratory event has
been detected at block 680, a confidence level of detecting a
respiratory event can be computed at block 682 and an estimated
severity of a respiratory event can be computed at block 684.
Blocks 682 and 684 can use any of the parameters described herein.
One example will be discussed in detail for illustrative purposes
with reference to Table 3.
TABLE-US-00003 TABLE 3 Parameter Event Time Output RR Apnea <10
sec. Gold 4 Point SpO2 Intermittent de-saturations Indicator Apnea
>15 sec. Gold 7 Point De-sat >3 points from baseline
Indicator Apnea >30 sec. Gold 12 Point De-sat >10 points from
baseline Indicator RR Apnea 10 Sec SuperNova Red SpO2 DeSat>10
Points from baseline or < Over 4 Point SpHb Hb <1pt X time
Indicator Apnea >15 Sec. SuperNova Red DeSat>10 Points from
baseline Over X 7 Point Decrement of Hb >1pt time Indicator
Apnea >30 sec. SuperNova Red DeSat>10 Points from baseline
Over X 12 Point Hb >1pt time Indicator
[0080] Table 3 provides an example of respiratory data from which a
confidence level and an estimated severity of a respiratory event
can be determined from one or more additional parameters. This
table provides example respiratory event indicators for when the
respiratory event detector 624 (FIG. 6A) detects an apnea event. As
shown in Table 3, a point indicator can represent an estimated
severity of a respiratory event. The point indicators shown in
Table 3 increase as the duration of an apnea event increases. The
additional respiratory parameters of SpO2 and SpHb can indicate a
confidence level in detecting a respiratory event. For example,
when an apnea event is of a certain duration, an SpO2 value
indicating that oxygen de saturation is more than a certain number
of points from baseline can reflect a higher confidence in
detecting a respiratory event. Specific SpO2 values required for
specific confidence levels can depend on the estimated severity of
an event, as also shown in Table 3. Moreover, an additional
parameter can indicate an even higher confidence level in detecting
a respiratory event. For example, when both SpO2 and SpHb values
satisfy predetermined thresholds a higher confidence level can
result.
[0081] In some embodiments, a point indicator representing an
estimated severity of a respiratory event can be based at least
partly on patient history data. The patient history data may be
obtained, for example, by the patient monitor 620 accessing an
electronic file that includes patient history data. Using the
patient history data, the respiratory event detector 624 can detect
whether a patient has a history of experiencing a detected
respiratory event, such as apnea. In some instances, the point
indicator can be decreased based on the patient history data
indicating that the patient has a pattern of experiencing the
detected respiratory event. For example, when an apnea event is
detected for a patient with a history of experiencing apnea, the
point indicator can be decreased relative to a patient without a
history of experiencing apnea. In other instances, the point
indicator can be increased based on the patient history data
indicating that the patient has a pattern of experiencing the
detected respiratory event. For example, if an abnormal SpO.sub.2
value is detected with an apnea event for patient with a history of
apnea, the point indicator can be increased relative to a patient
without a history of apnea.
[0082] Adjusting the point indicator based on patient history data
can be advantageous because clinicians with knowledge of patient
history can tend to ignore alarms related to known conditions,
especially when such alarms frequently trigger. These alarms may be
referred to as nuisance alarms. A patient monitor that provides a
point indicator based on patient history data can better indicate a
severity of a respiratory event in some instances. Consequently,
such a point indicator can help a clinician better prioritize which
alarms to respond to and/or provide an appropriate level of care to
a detected respiratory event. The point indicator can therefore
lead to improved care for patients.
[0083] One or more respiratory event indicators, such as the
indicators described with reference to Table 3, can be output along
with respiratory rate at block 686. Additional physiological
parameters, including any of the parameters mentioned above, can
also be output at block 688. FIGS. 7 and 8 provide example displays
that include some of the additional physiological parameters.
[0084] If a respiratory event is not detected at decision block
680, then respiratory rate can be output at block 690. Additional
physiological parameters, including any of the parameters mentioned
above, can also be output at block 692.
[0085] FIG. 7 illustrates an example noninvasive multiparameter
physiological monitor 700 that can implement any of the features
described herein, for example, the features of the respiratory
monitoring system 600A. An embodiment of the monitor 700 includes a
display 701 showing data for multiple physiological parameters. For
example, the display 701 can include a CRT or an LCD display
including circuitry similar to that available on physiological
monitors commercially available from Masimo Corporation of Irvine,
Calif. sold under the name Radical.TM., and disclosed in U.S. Pat.
Nos. 7,221,971; 7,215,986; 7,215,984 and 6,850,787, for example,
the disclosures of which are hereby incorporated by reference in
their entirety. However, many other display components can be used
that are capable of displaying respiratory rate and other
physiological parameter data along with the ability to display
graphical data such as plethysmographs, respiratory waveforms,
trend graphs or traces, respiratory event indicators, and the
like.
[0086] The depicted embodiment of the display 701 includes a
measured value of respiratory rate 714 in breaths per minute (bpm)
and a respiratory rate waveform graph 706. The respiratory rate
waveform graph 706 can include any of respiratory event indicators
described herein. In addition, other measured blood constituents
shown include SpO.sub.2 702, a pulse rate 704 in beats per minute
(BPM), and a perfusion index 708. Many other blood constituents or
other physiological parameters can be measured and displayed by the
multiparameter physiological monitor 700, such as blood pressure,
ECG readings, EtCO.sub.2 values, bioimpedance values, and the like.
In some embodiments, multiple respiratory rates, corresponding to
the multiple input sensors and/or monitors, can be displayed.
[0087] FIG. 8 illustrates another example multiparameter
physiological monitor display 801. The display 801 can output a
multiparameter confidence indicator 814. The multiparameter
confidence indicator 814 can be generated using any of the
techniques described above.
[0088] Referring to FIG. 8, another example display 801 is shown
that includes parameter data for respiratory rate, including a
measured respiratory rate value 812 in breaths per minute (bpm) and
a respiratory waveform graph 806 that includes respiratory event
indicators. These respiratory indicators can represent an estimated
severity of a respiratory event. A confidence level indicator 814
can separately indicate the confidence level in detecting the
respiratory event. The display 801 also includes parameter data for
SpO.sub.2 802, pulse rate 804 in beats per minute (BPM), and SpHb
816 in grams per deciliter (g/dL). A respiratory event
multiparameter confidence indicator 814 is also depicted. In the
depicted embodiment, the multiparameter confidence indicator 814
includes text that indicates that the detected respiratory event
has a low multiparameter confidence level in a detection of the
respiratory event.
[0089] The example displays of FIGS. 4, 7, and 8 are merely
illustrative examples. Many other variations and combinations of
respiratory event indicators are also possible in other
implementations without departing from the spirit and/or scope of
the disclosure.
[0090] The example displays of FIGS. 4, 7, and 8 can display
respiratory event indicators from a number of rating systems. FIG.
9 illustrates a rating system 900. The rating system 900 is an
example rating system that can simultaneously convey both a
significance of a respiratory event and a confidence in the
occurrence of the respiratory event using a single respiratory
indicator. The rating system 900 can be implemented in any of the
respiratory monitoring systems described above and displayed using
any of the displays described above.
[0091] The confidence level in detecting a respiratory event can be
represented by, for example, a color, a pattern, or any of the
methods provided herein, for example, in connection with FIG. 3. As
shown in FIG. 9, Cross Hatched indicates a lower confidence level
and Super Nova indicates the highest confidence level. Each of the
different confidence levels of FIG. 9 includes a different pattern
inside a shape that indicates an estimated severity. An example of
what each confidence level can represent is provided in Table
4.
TABLE-US-00004 TABLE 4 Input/Variable Examples Confidence Level Any
Star without Patient Demographics or History Crosshatched Single
Parameter (e.g. Respiration) Silver Dual Parameter (e.g.
Respiration and Oxygen Gold Saturation) Tri Parameter (e.g.
Respiration, Oxygen Saturation, Supernova (Red) and Total
Hemoglobin)
[0092] The estimated severity of a respiratory event can be
represented by, for example, a shape or any of the alternatives
provided herein, for example, in connection with FIG. 3. As shown
in FIG. 9, the number of points on a shape can represent a
corresponding estimated severity. For example, a respiratory event
with an estimated severity of 7 points can be represented by a 7
point star and another respiratory event with an estimated severity
of 12 can be represented by a 12 point star. Thus, the rating
system 900 can be referred to as a star point system because the
number points of a star can indicate an estimated severity.
[0093] Rating systems, such as the rating system 900, can be
designed to be so that visually impaired people can easily decipher
them. For example, someone who is color blind can decipher the
rating system 900 based on the number of points on a star and the
pattern inside of the star. The pattern can have either protanope,
deuteranope, or tritanope color variations to assist the color
blind.
Additional Embodiments
[0094] Although some of the sensors disclosed herein, such as the
acoustic sensor 103 in the sensor system 200 of FIG. 2, have been
described with reference to wired implementations for illustrative
purposes, it will be understood that any of the sensors described
herein can communicate wirelessly to a patient monitor or other
device. For instance, any of the sensors described herein can
transmit and/or receive data wirelessly via any suitable
communication protocol, such as WiFi, Bluetooth, or the like.
[0095] Moreover, any of the displays described herein, such as the
example displays of FIGS. 4, 7, and 8, can be configured to receive
user input to enable a user to select one or more respiratory event
indicators. If the display is a touch screen, for instance, a
clinician or other user can touch a respiratory event indicator to
obtain more information. Other input methods are also possible. In
response to the user selection, the one or more processors can then
provide additional detail regarding the selected respiratory event
indicator(s). This information can advantageously provide a
clinician with a more complete picture of a patient's condition.
More generally, a clinician can select any point on a respiratory
trend graph to obtain additional physiological information.
[0096] For instance, in response to the user selection of a
respiratory event or other point on a respiratory trend, the one or
more processors can cause display of the parameter(s) and/or
different indicator(s) from which a severity and/or calculated
confidence were determined. Some examples of information that can
be displayed can include an audio recording of a patient's
respiration corresponding to the time the respiratory event
occurred or within a range of time around the occurrence of the
event, a display of an audio waveform corresponding to the time (or
time range) when the respiratory event occurred, raw sensor data,
respiratory rate values, SpO.sub.2 values, SpHb values, the like,
or any combination thereof. According to some implementations, the
parameter(s) and/or the different indicator(s) can be output at
block 688 of the process 600B.
[0097] In some embodiments, the patient monitor system can prompt a
user, such as a clinician, for information regarding respiratory
indicators shown on a display and/or enable a user to enter such
information via the display. For example, the patient monitor
system can prompt a clinician for feedback regarding the clinical
significance of one or more particular respiratory event indicators
and save the information provided by the clinician to
non-transitory memory for later use. Such feedback can be useful,
for example, to refine algorithms to detect a respiratory event,
determine a severity of a respiratory event, calculate a confidence
in detecting a respiratory event, the like, or any combination
thereof. As one example, this feedback information can be used
automatically by a processor to differentiate noise in data
generated by a sensor from a respiratory event. As another example,
feedback data regarding respiratory events can be collected and
analyzed for subsequent adjustment of respiratory rate calculation
algorithms or respiratory event detection algorithms.
Terminology
[0098] The modules described herein of certain embodiments can be
implemented as software modules, hardware modules, or a combination
thereof. In general, the word "module," as used herein, can refer
to logic embodied in hardware or firmware or to a collection of
software instructions executable on a processor. Additionally, the
modules or components thereof can be implemented in analog
circuitry in some embodiments.
[0099] Conditional language used herein, such as, among others,
"can," "could," "might," "can," "e.g.," and the like, unless
specifically stated otherwise, or otherwise understood within the
context as used, is generally intended to convey that certain
embodiments include, while other embodiments do not include,
certain features, elements and/or states. Thus, such conditional
language is not generally intended to imply that features, elements
and/or states are in any way required for one or more embodiments
or that one or more embodiments necessarily include logic for
deciding, with or without author input or prompting, whether these
features, elements and/or states are included or are to be
performed in any particular embodiment.
[0100] Depending on the embodiment, certain acts, events, or
functions of any of the methods described herein can be performed
in a different sequence, can be added, merged, or left out all
together (e.g., not all described acts or events are necessary for
the practice of the method). Moreover, in certain embodiments, acts
or events can be performed concurrently, e.g., through
multi-threaded processing, interrupt processing, or multiple
processors or processor cores, rather than sequentially.
[0101] The various illustrative logical blocks, modules, circuits,
and algorithm steps described in connection with the embodiments
disclosed herein can be implemented as electronic hardware,
computer software, or combinations of both. To clearly illustrate
this interchangeability of hardware and software, various
illustrative components, blocks, modules, circuits, and steps have
been described above generally in terms of their functionality.
Whether such functionality is implemented as hardware or software
depends upon the particular application and design constraints
imposed on the overall system. The described functionality can be
implemented in varying ways for each particular application, but
such implementation decisions should not be interpreted as causing
a departure from the scope of the disclosure.
[0102] The various illustrative logical blocks, modules, and
circuits described in connection with the embodiments disclosed
herein can be implemented or performed with a general purpose
processor, a digital signal processor (DSP), an application
specific integrated circuit (ASIC), a field programmable gate array
(FPGA) or other programmable logic device, discrete gate or
transistor logic, discrete hardware components, or any combination
thereof designed to perform the functions described herein. A
general purpose processor can be a microprocessor, but in the
alternative, the processor can be any conventional processor,
controller, microcontroller, or state machine. A processor can also
be implemented as a combination of computing devices, e.g., a
combination of a DSP and a microprocessor, a plurality of
microprocessors, one or more microprocessors in conjunction with a
DSP core, or any other such configuration.
[0103] The blocks of the methods and algorithms described in
connection with the embodiments disclosed herein can be embodied
directly in hardware, in a software module executed by a processor,
or in a combination of the two. A software module can reside in RAM
memory, flash memory, ROM memory, EPROM memory, EEPROM memory,
registers, a hard disk, a removable disk, a CD-ROM, or any other
form of computer-readable storage medium known in the art. An
exemplary storage medium is coupled to a processor such that the
processor can read information from, and write information to, the
storage medium. In the alternative, the storage medium can be
integral to the processor. The processor and the storage medium can
reside in an ASIC. The ASIC can reside in a user terminal. In the
alternative, the processor and the storage medium can reside as
discrete components in a user terminal.
[0104] While the above detailed description has shown, described,
and pointed out novel features as applied to various embodiments,
it will be understood that various omissions, substitutions, and
changes in the form and details of the devices or algorithms
illustrated can be made without departing from the spirit of the
disclosure. As will be recognized, certain embodiments of the
inventions described herein can be embodied within a form that does
not provide all of the features and benefits set forth herein, as
some features can be used or practiced separately from others. The
scope of certain inventions disclosed herein is indicated by the
appended claims rather than by the foregoing description. All
changes which come within the meaning and range of equivalency of
the claims are to be embraced within their scope.
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